We introduce a method for flexible shape registration using Bayesian changepoint analysis. Our approach is particularly suitable for shapes containing “hinge”-like flexibility, and is motivated by problems in structural proteomics and bioinformatics. We define a class of flexible shape spaces and an associated Procrustes-type metric, along with a highly efficient algorithm for computing flexible shape distance. We use this distance to define distributions over flexible shapes, and develop Bayesian models for several variants including both affine and rigid-body component transformations. We demonstrate the approach on several examples arising from protein structure analysis, including structure alignment and function discovery.

This un-edited manuscript has been accepted for publication in Biophysical Journal and is freely available on BioFAST at www.biophysj.org. The final copyedited version of the paper may be found at www.biophysj.org.

Research article Large-scale evaluation of dynamically important residues in proteins predicted by the perturbation analysis of a coarse-grained elastic model